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Kenneth Fu

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Proceedings Article | 29 January 2010 Paper

The application of wavelet denoising in material discrimination system

Kenneth Fu, Dale Ranta, Clark Guest, Pankaj Das

Proceedings Volume 7538, 75380Z (2010) https://doi.org/10.1117/12.838648

KEYWORDS: X-rays, X-ray imaging, Wavelets, Signal attenuation, Image fusion, Denoising, Photons, Inspection, Imaging systems, Dual energy imaging

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Recently, the need for cargo inspection imaging systems to provide a material discrimination function has become desirable. This is done by scanning the cargo container with x-rays at two different energy levels. The ratio of attenuations of the two energy scans can provide information on the composition of the material. However, with the statistical error from noise, the accuracy of such systems can be low. Because the moving source emits two energies of x-rays alternately, images from the two scans will not be identical. That means edges of objects in the two images are not perfectly aligned. Moreover, digitization creates blurry-edge artifacts. Different energy x-rays produce different edge spread functions. Those combined effects contribute to a source of false classification namely, the "edge effect." Other types of false classification are caused by noise, mainly Poisson noise associated with photons. The Poisson noise in xray images can be dealt with using either a Wiener filter or a wavelet shrinkage denoising approach. In this paper, we propose a method that uses the wavelet shrinkage denoising approach to enhance the performance of the material identification system. Test results show that this wavelet-based approach has improved performance in object detection and eliminating false positives due to the edge effects.

Proceedings Article | 29 January 2010 Paper

Layer separation for material discrimination cargo imaging system

Kenneth Fu, Dale Ranta, Pankaj Das, Clark Guest

Proceedings Volume 7538, 75380Y (2010) https://doi.org/10.1117/12.838646

KEYWORDS: Signal attenuation, X-rays, Metals, Lead, Imaging systems, X-ray imaging, Dual energy imaging, Photons, Copper, Image segmentation

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We propose an approach to boost the accuracy of the performance of a high-energy x-ray material discrimination imaging system. The theory of using two energies of x-rays to scan objects to extract the atomic information has been well developed. Such an approach is known as dual-energy imaging. At the beginning of this century, mega-volt-level dual-energy systems began to be applied to extract information regarding the materials inside a cargo container. For a system that scans at two x-ray energies, the ratio between the attenuations of the two energies will be different for different materials. Using this property, we can classify the content of a cargo container from the attenuation ratio image. However, thick shielding can reduce the signal-to-noise ratio such that correct material identification with low false alarm rate is unfeasible without further image processing. We have developed a method for high atomic number discrimination that can more accurately identify a region of high atomic number. The pixels of each object are clustered using our proposed clustering approach. The thickness and ratio of high- and low-energy attenuations of each object can then be more correctly calculated by separating it from its background. Our method can significantly improve the accuracy by suppressing false alarms and increasing the detection rate.

Proceedings Article | 4 September 2009 Paper

A novel algorithm for material discrimination using a dual energy imaging system

Kenneth Fu, Dale Ranta, Clark Guest, Pankaj Das

Proceedings Volume 7445, 744513 (2009) https://doi.org/10.1117/12.825344

KEYWORDS: X-rays, Signal attenuation, X-ray imaging, Image segmentation, Photons, Imaging systems, Lead, Image processing, Dual energy imaging, Sensors

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Dual energy imaging is a technique whereby an object is scanned with X-rays of two interleaved energies to extract information about the object's atomic composition (Z). This technique is based on the fact that the X-ray absorption coefficient decreases with X-ray energy for low-Z materials, but begins to increase for high-Z materials due to the onset of pair production. Methods using the ratio of the attenuations of high-energy to low-energy images as an indicator of Z value have been proposed by several people. However, the statistical errors associated with the systems make those indicators unreliable. Methods that calculate the probability of high Z encounter a problem of what is the threshold probability to call high Z to minimize both miss and false alarm. We have developed an "adaptive regional masking" method that avoids the predicament of a single threshold. Our method is adaptive because the threshold for determining high Z varies adaptively in different regions on the image. The "mask" refers to the location of objects in the thickness map that mask to possible high-Z regions. Adaptive thresholding improves detection, while masking reduces false alarms. Test results show an increased accuracy of high-Z detection using this approach. In this paper, we discuss the approach and show some sample test results illustrating the effectiveness of the method.

Proceedings Article | 4 September 2009 Paper

Segmentation of suspicious objects in an x-ray image using automated region filling approach

Kenneth Fu, Clark Guest, Pankaj Das

Proceedings Volume 7445, 744510 (2009) https://doi.org/10.1117/12.826338

KEYWORDS: Image segmentation, X-ray imaging, X-rays, Binary data, Image processing algorithms and systems, Lead, Inspection, Imaging systems, Vector spaces, Tungsten

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To accommodate the flow of commerce, cargo inspection systems require a high probability of detection and low false alarm rate while still maintaining a minimum scan speed. Since objects of interest (high atomic-number metals) will often be heavily shielded to avoid detection, any detection algorithm must be able to identify such objects despite the shielding. Since pixels of a shielded object have a greater opacity than the shielding, we use a clustering method to classify objects in the image by pixel intensity levels. We then look within each intensity level region for sub-clusters of pixels with greater opacity than the surrounding region. A region containing an object has an enclosed-contour region (a hole) inside of it. We apply a region filling technique to fill in the hole, which represents a shielded object of potential interest. One method for region filling is seed-growing, which puts a "seed" starting point in the hole area and uses a selected structural element to fill out that region. However, automatic seed point selection is a hard problem; it requires additional information to decide if a pixel is within an enclosed region. Here, we propose a simple, robust method for region filling that avoids the problem of seed point selection. In our approach, we calculate the gradient Gx and Gy at each pixel in a binary image, and fill in 1s between a pair of x₁ _Gx(x1,y)=-1 and x₂ _Gx(x2,y)=1, and do the same thing in y-direction. The intersection of the two results will be filled region. We give a detailed discussion of our algorithm, discuss the strengths this method has over other methods, and show results of using our method.

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